No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

Wang, Hui; Li, Chaofeng; Guan, Tuxin; Zhao, Shenghu

doi:10.1016/j.displa.2021.102058

Cited by 17 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These were mapped onto perceptual quality scores with a trained support vector regressor (SVR). NSS-based methods have been proposed in other transform domains, i.e., curvelet [11], contourlet [12], quaternion wavelet transform [13] and discrete cosine transform [14] and in the spatial domain [15], as well.…”

Section: Nss-based Methodsmentioning

confidence: 99%

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Varga

2021

Applied Sciences

View full text Add to dashboard Cite

No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.

show abstract

Section: Nss-based Methodsmentioning

confidence: 99%

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Varga

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Grigoryan and Agaian [103] proposed an image restoration model with the Wiener filter and quaternion Fourier transform, which can handle denoising and deblurring tasks. Wang et al [104] applied the QWT for a no-reference stereoscopic image quality assessment. Other transforms, such as the quaternion polar harmonic transform [105] and discrete wavelet transform [106], were also extended to the quaternion domain with better performance.…”

Section: Transformation-based Modelsmentioning

confidence: 99%

Review of Quaternion-Based Color Image Processing Methods

Huang

Gao

2023

Mathematics

View full text Add to dashboard Cite

Images are a convenient way for humans to obtain information and knowledge, but they are often destroyed throughout the collection or distribution process. Therefore, image processing evolves as the need arises, and color image processing is a broad and active field. A color image includes three distinct but closely related channels (red, green, and blue (RGB)). Compared to directly expressing color images as vectors or matrices, the quaternion representation offers an effective alternative. There are several papers and works on this subject, as well as numerous definitions, hypotheses, and methodologies. Our observations indicate that the quaternion representation method is effective, and models and methods based on it have rapidly developed. Hence, the purpose of this paper is to review and categorize past methods, as well as study their efficacy and computational examples. We hope that this research will be helpful to academics interested in quaternion representation.

show abstract

“…The diminution is similar for both datasets. Metrics Waterloo-P1/Waterloo-P2 Waterloo-P2/Waterloo-P1 Liu [12] 0.696 0.701 Yang [14] 0.781 0.864 Chen [22] 0.806 0.846 Saliency-SIQA [17] 0.826 0.848 DECOSINE [13] 0.842 0.873 Wang [15] 0.856 0.881 Proposed 0.944 0.940 0.9. Compared to the second best metric (i.e., Wang), the improvement accuracy of quality assessment was found to be 10% in terms of PLCC.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

“…Then, series of feature extraction have been conducted from these images. Recently in [15], monocular and binocular quality features, including texture and energy features, are first retrieved, and then ensemble learning is utilized to map the quality score. Meanwhile, in [16] a multi-task CNN model is proposed to extract NSS features as an auxiliary task, and to produce quality score as primary task.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Messai

Chetouani

2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multiscore-SIQA

show abstract

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

Cited by 17 publications

References 45 publications

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

Review of Quaternion-Based Color Image Processing Methods

End-to-End Deep Multi-Score Model for No-Reference Stereoscopic Image Quality Assessment

Contact Info

Product

Resources

About